Someone can explain to me what's the expectations for these AI labs?
I mostly see their products as commodity at this point, with strong open source contenders.
Eventually it will become hard to justify the premium on these models.
I mostly see their products as commodity at this point, with strong open source contenders.
Eventually it will become hard to justify the premium on these models.
As people keep pointing out, the moat is insufficient to ward off international or domestic competitors.
So the answer is to try to seek regulatory capture.
Because, as OpenAI is learning [1], you still need to sell it. The tech giants have a seat at the table is mostly because they have distribution down.
[1] https://www.cnbc.com/2026/02/23/open-ai-consulting-accenture...
Now if "fully caught up" means today's level of intelligence is available for free in two years, by then that level of intelligence means very little
The only thing I can see them meaning is what you said, "in a minute the stragglers will be where the leaders were a minute ago", which, yeah, sure.
Or AGI hits and this theory collapses, but that's feeling less likely every day.
Play out a scenario. An open source model is released that is capable as Mythos. Presumably it requires hardware big enough that running it at home is unfeasible. You are imagining that individuals can run it in the cloud themselves for cheaper than api tokens would cost? Or even small companies? And that Anthropic and OpenAI won't be able to cut costs deeper than their competitors while staying profitable?
If it is fundamentally a commodity, that means "running it yourself" also isn't really interesting as a proposition. Many of the world's biggest companies sell commodities. It's a great business to be in if you can sell them cheaper than anyone else.
The value add here isn't the model, it is "having a bunch of compute and using it more efficiently than anyone else".
If Mythos is the endgame, companies won't release open-weight equivalents, and no private individuals have the capital to train such models.
I expect that people on subscriptions can be asked to donate 1 query a month towards an open source distillery.
It should be good enough to distill SOTA models over time.
The result won't be perfect, but it will be close.
Think SETI@home, but it'll be model distillation instead.
Companies bake their workflows into these tools. Internal processes start to be written up around specific tools. Once something works, it gets pushed out at scale for all to copy.
Anthropic hit $30B in revenue and this is just the start of coding being deployed at scale. Hard to look past these numbers at this point
[1] https://x.com/kenshii_ai/status/2046111873909891151/photo/2
Tokens will continue to increase in price until the supply meets the demand. That's going to take a while.
[0]: https://www.tomshardware.com/pc-components/gpus/datacenter-g...
[1]: https://www.cnbc.com/2025/11/14/ai-gpu-depreciation-coreweav...
GPUs do not burn out in three years, H100 rentals are priced at the same level as two years ago, and are effectively sold out. [1]
[0] https://news.ycombinator.com/item?id=46203986#46208221
[1] https://newsletter.semianalysis.com/p/the-great-gpu-shortage...
This is completely not true if you use AWS Bedrock, and applies to both your private that or in a business context. Its one of their core arguments for the service use.
[1] - "...At Amazon, we don’t use your prompts and outputs to train or improve the underlying models in Amazon Bedrock and SageMaker JumpStart (including those from third parties), and humans won’t review them. Also, we don’t share your data with third-party model providers. Your data remains private to you within your AWS accounts..."
[1] - https://aws.amazon.com/blogs/security/securing-generative-ai...
The data isn't the sole point of them, they also are about bringing in users that will encourage the product use in companies and ultimately drive more profitable API adoption within their orgs, and just general diffuse mindshare doing the same.
You can still opt out (except with Google's offering which disables lots of features if you opt out of training).
Here is the thing nobody wants to say out loud or they are too dumb to realize. AI is intelligence, and intelligence has almost never been the binding constraint on productivity.
So you will get no productivity increase from the AI bubble. Yes, you read that correctly.
The test is simple, if raw brainpower were the bottleneck, you could 10x any company by hiring 200 PhDs. In practice you get 200 brilliant people writing unread memos, refactoring things that worked, and forming a committee to rename the committee. Smart has always been cheaper and more abundant than the discourse pretends.
Every real productivity revolution came from somewhere else like energy (steam, electricity), capital stock (machines that do the physical work), or coordination (railroads, shipping containers, the assembly line, the internet).
None of these raised the average IQ of the workforce, they changed what a given worker could move, reach, or coordinate with. Solow old line basically still holds. The output per worker grows when you give the worker better tools and infrastructure, not better neurons.
Meanwhile the actual bottlenecks in a modern firm are regulatory approval, legacy systems, procurement cycles, customer adoption, internal politics, and physical supply chains that don't care how clever your email was. A smart brains intern at every desk produces more artifacts, not more throughput, and in a lot of organizations, more artifacts is actively negative ROI.
Jevons does not save you either, cheaper cognition mostly means more slide decks, not more GDP.
So the setup is that models are commoditizing on one side, and on the other side a product whose core value add (more intelligence, faster) is aimed at a constraint that was never really binding. This of course a rough combo for a trillion dollar capex supercycle.
Fun for the trade, while it lasts, but there is no thesis. Just dont tell CNBC and short NVDA on time ,-)
Granted LLM's are not even PHDs.
What a weird time we live in...
Exactly. We don't use the intelligence we already have! That seems to be the real problem with the "AGI" concept. Given such a capability, we'll just nerf it, gatekeep it, and/or bias it. There's no reason to think we'll actually use it to benefit humanity as a whole. It will be shaped into an instrument to enforce our prejudices.
There's also a very strong Trurl and Klapaucius [1] component to this AI craziness, as in I remember a passage in Lem's The Cyberiad where either Trurl or Klapaucius were "discussing" with an intelligent/AGI robot and asking it for stuff-to-know/information, at which point said AGI robot started literally inundating them with information, paper on top of paper on top of paper of information. At that point it doesn't even matter if that information is correct or smart or whatever, because by that point the very amount of said information has changed everything into a futile endeavour.
I have seen this argument made a lot, but llm serving being a commodity makes it _better_ for them not worse.
If it's a commodity, then you are entirely competing on price, and the players that will win on price will be the largest ones, because they can find efficiencies that smaller competitors won't have.
It's actually the small LLM companies that are in trouble if LLM serving commoditizes. They will need to distinguish themselves on features, because they can't compete on price. And even there the big labs will have an advantage.
As the US sold weapons to many nations in the past, so will China, the US, France, etc sell AI cyber capability to other nations. Likely every modern nation will need some datacenter to host a cluster of the preferred vendor, as nobody's going to trust the US or China with their security.
> Eventually it will become hard to justify the premium on these models.
On the contrary, the model is the moat.
The model represents embodied capital expenditure in the form of training. Training is not free, and it is not a commodity, it is heavily influence by curation.
Eventually the ever-increasing training expense will reduce the competition to 2-3 participants running cutting edge inference. Nobody else will be able to afford the chips, watts, and warehouse. It's a physics problem - not a lack of will.
If you're a retail user, and a lower-tier model is suitable for your work, you'll have commodity LLM's to help you. Deprecated models running on tired silicon. Corporate surveillance and ad-injection.
But if you're working on high-stakes problems in real time, you're going to want the best money can buy, so you'll concentrate your spend on the cutting-edge products, open API's, a suite of performance monitoring tools and on-the-fly engineering support. And since the cutting edge is highly sought after, it's a seller's market. The cutting edge products buoyed by institutional spend will pull away from the pack. Their performance will far exceed what you're using, because your work isn't important. Hockey stick curve. Haves and Have-Nots.
The economic reality is predetermined by today's physical constraints - paradigm shifting breakthroughs in quantum computing and superconductors could change the calculus but, like atomic fusion power, don't count on it being soon.
it will be interesting to see it unfold